Gas stove and respiratory health: a cross-sectional study and a cohort study in Denmark, 2000–2018
Bibliographic record
Abstract
BACKGROUND: Knowledge on gas stove and adult respiratory health is limited. We studied the association between having a gas stove or not and respiratory health using cross-sectional and cohort designs. METHODS: Information on gas stove and respiratory symptoms (e.g. bronchitis, asthma, cough) was obtained from the Danish Health and Morbidity Survey year 2000 (aged ≥ 16 years, n = 3491). Odds ratios (ORs) of respiratory symptoms were estimated by Logistic regressions. In the cohort study, 3444 asthma-free individuals were followed for up to 18 years, with incident asthma identified via Danish National Registers. Incidence rate ratios (IRRs) were estimated by Poisson regression of incidence rates (IRs). RESULTS: Overall, 13.9% of participants had a gas stove. Respiratory symptoms ranged from 3.0% (bronchitis) to 12.1% (cough). The odds were significantly higher for bronchitis (OR 1.90; 95% CI, 1.15-3.14), cough (OR 1.46; 95% CI, 1.11-1.91), and shortness of breath (OR 1.89: 95% CI, 1.30-2.77) among participants with gas stoves compared to participants without. In the cohort study the overall IR of asthma was 6.1 per 1000 person-years. The adjusted IRR of asthma was 1.19 (95% CI, 0.78-1.80). CONCLUSIONS: Having a gas stove was associated with higher risk of certain respiratory symptoms and indicated an increased asthma incidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".